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-rw-r--r--tests/validation/CL/RNNLayer.cpp138
-rw-r--r--tests/validation/fixtures/RNNLayerFixture.h145
2 files changed, 283 insertions, 0 deletions
diff --git a/tests/validation/CL/RNNLayer.cpp b/tests/validation/CL/RNNLayer.cpp
new file mode 100644
index 0000000000..0af6f8ea00
--- /dev/null
+++ b/tests/validation/CL/RNNLayer.cpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/runtime/CL/functions/CLRNNLayer.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/RNNLayerDataset.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/RNNLayerFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_f32(0.001f);
+RelativeTolerance<half> tolerance_f16(half(0.1));
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(RNNLayer)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8, 0), // Wrong data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Wrong input size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong weights size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong recurrent weights size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong bias size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong output size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong hidden output size
+ }),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(30U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ })),
+ framework::dataset::make("Expected", { false, false, false, false, false, false, false })),
+ input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected)
+{
+ ARM_COMPUTE_EXPECT(bool(CLRNNLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &hidden_output_info.clone()->set_is_resizable(false), info)) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+template <typename T>
+using CLRNNLayerFixture = RNNLayerValidationFixture<CLTensor, CLAccessor, CLRNNLayer, T>;
+
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLRNNLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // FP32
+
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLRNNLayerFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE_END() // RNNLayer
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/fixtures/RNNLayerFixture.h b/tests/validation/fixtures/RNNLayerFixture.h
new file mode 100644
index 0000000000..42b99cce1c
--- /dev/null
+++ b/tests/validation/fixtures/RNNLayerFixture.h
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE
+#define ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE
+
+#include "tests/Globals.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/reference/ActivationLayer.h"
+#include "tests/validation/reference/ArithmeticAddition.h"
+#include "tests/validation/reference/FullyConnectedLayer.h"
+#include "tests/validation/reference/GEMM.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class RNNLayerValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape recurrent_weights_shape, TensorShape bias_shape, TensorShape output_shape, ActivationLayerInfo info,
+ DataType data_type)
+ {
+ _target = compute_target(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type);
+ _reference = compute_reference(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
+ library->fill(tensor, distribution, i);
+ }
+
+ TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
+ ActivationLayerInfo info, DataType data_type)
+ {
+ // Create tensors
+ TensorType input = create_tensor<TensorType>(input_shape, data_type);
+ TensorType weights = create_tensor<TensorType>(weights_shape, data_type);
+ TensorType recurrent_weights = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+ TensorType bias = create_tensor<TensorType>(bias_shape, data_type);
+ TensorType hidden_state = create_tensor<TensorType>(output_shape, data_type);
+ TensorType output = create_tensor<TensorType>(output_shape, data_type);
+
+ // Create and configure function
+ FunctionType rnn;
+ rnn.configure(&input, &weights, &recurrent_weights, &bias, &hidden_state, &output, info);
+
+ ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ input.allocator()->allocate();
+ weights.allocator()->allocate();
+ recurrent_weights.allocator()->allocate();
+ bias.allocator()->allocate();
+ hidden_state.allocator()->allocate();
+ output.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(input), 0);
+ fill(AccessorType(weights), 0);
+ fill(AccessorType(recurrent_weights), 0);
+ fill(AccessorType(bias), 0);
+ fill(AccessorType(hidden_state), 0);
+
+ // Compute function
+ rnn.run();
+
+ return output;
+ }
+
+ SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape,
+ const TensorShape &output_shape, ActivationLayerInfo info, DataType data_type)
+ {
+ // Create reference
+ SimpleTensor<T> input{ input_shape, data_type };
+ SimpleTensor<T> weights{ weights_shape, data_type };
+ SimpleTensor<T> recurrent_weights{ recurrent_weights_shape, data_type };
+ SimpleTensor<T> bias{ bias_shape, data_type };
+ SimpleTensor<T> hidden_state{ output_shape, data_type };
+
+ // Fill reference
+ fill(input, 0);
+ fill(weights, 0);
+ fill(recurrent_weights, 0);
+ fill(bias, 0);
+ fill(hidden_state, 0);
+
+ TensorShape out_shape = recurrent_weights_shape;
+ out_shape.set(1, output_shape.y());
+
+ // Compute reference
+ SimpleTensor<T> out_w{ out_shape, data_type };
+ SimpleTensor<T> fully_connected = reference::fully_connected_layer(input, weights, bias, out_shape);
+ SimpleTensor<T> gemm = reference::gemm(hidden_state, recurrent_weights, out_w, 1.f, 0.f);
+ SimpleTensor<T> add_res = reference::arithmetic_addition(fully_connected, gemm, data_type, ConvertPolicy::SATURATE);
+ return reference::activation_layer(add_res, info);
+ }
+
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE */
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